I've fitted a lognormal model using R with a set of data. The resulting parameters were:
meanlog = 4.2991610 sdlog = 0.5511349
I'd like to transfer this model to Scipy, which I've never used before. Using Scipy, I was able to get a shape and scale of 1 and 3.1626716539637488e+90 -- very different numbers. I've also tried to use the exp of the meanlog and sdlog but continue to get bizarre graph.
I've read every doc I can on scipy and am still confused about what the shape and scale parameters mean in this instance. Would it just make sense to code the function myself? That seems prone to errors though, as I'm new to scipy.
SCIPY Lognormal (BLUE) vs. R Lognormal (RED):
Any thoughts on what direction to take? The data is fit very well with the R model, by the way, so if it looks like something else in Python, feel free to share.
I'm running Scipy 0.11
Here's a subset of the data. The actual sample is 38k+, with a mean of 81.53627:
[60, 170, 137, 138, 81, 140, 78, 46, 1, 168, 138, 148, 145, 35, 82, 126, 66, 147, 88, 106, 80, 54, 83, 13, 102, 54, 134, 34]
I am working on a function to capture the pdf:
def lognoral(x, mu, sigma): a = 1 / (x * sigma * numpy.sqrt(2 * numpy.pi) ) b = - (numpy.log(x) - mu) ^ 2 / (2 * sigma ^ 2) p = a * numpy.exp(b) return p
However, this give me numbers the following (I tried several in case I was getting the meaning of sdlog and meanlog mixed up):
>>> lognormal(54,4.2991610, 0.5511349) 0.6994656085799437 >>> lognormal(54,numpy.exp(4.2991610), 0.5511349) 0.9846125119455129 >>> lognormal(54,numpy.exp(4.2991610), numpy.exp(0.5511349)) 0.9302407837304372
rerunning with "UPQuark's" suggestion:
shape, loc, scale (1.0, 50.03445923295007, 19.074457156766517)
The shape of the graph is very similar, however, with the peak happening around 21.